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Record W2012457787 · doi:10.1007/s10995-012-1153-1

A Comparison Between Late Preterm and Term Infants on Breastfeeding and Maternal Mental Health

2012· article· en· W2012457787 on OpenAlexafffundabout
Sheila McDonald, Karen Benzies, Jenna E. Gallant, Deborah McNeil, Siobhan M. Dolan, Suzanne Tough

Bibliographic record

VenueMaternal and Child Health Journal · 2012
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsAlberta Health ServicesUniversity of Calgary
FundersAlberta Innovates
KeywordsMedicineBreastfeedingPostpartum depressionMental healthLongitudinal studyAnxietyPublic healthPediatricsProspective cohort studyDepression (economics)PopulationCohort studyDemographyPregnancyPsychiatryEnvironmental healthNursing

Abstract

fetched live from OpenAlex

The objective of this study was to compare breastfeeding, postpartum mental health, and health service utilization between a group of late preterm (LP) maternal infant pairs and term counterparts. Data was drawn from a prospective community-based cohort in Calgary, Alberta. Bivariate and multivariable analyses were performed. LP infants were more likely to have had a longer median length of stay after birth (P < 0.001) and a higher re-hospitalization rate at 4-months (P < 0.001) compared to term infants. Mothers of LP infants were more likely to report immediate breastfeeding difficulties (P < 0.001) and earlier cessation of breastfeeding at 4-months postpartum (P = 0.008). Multivariable analyses revealed that LP status was an independent risk factor for excessive symptoms of maternal anxiety (OR = 2.07; 95 % CI = 1.08,3.98), but not for depression, stress, or low parenting morale. LP infants and their families are a vulnerable population with unique developmental trajectories. Further longitudinal research is required.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.322
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations79
Published2012
Admission routes3
Has abstractyes

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